{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "4ba6aba8" }, "source": [ "# 🤖 **Data Collection, Creation, Storage, and Processing**\n" ] }, { "cell_type": "markdown", "metadata": { "id": "jpASMyIQMaAq" }, "source": [ "## **1.** 📦 Install required packages" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "f48c8f8c", "outputId": "5eab5d3d-d345-4d7d-b644-d7c5bc55c88d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.12/dist-packages (4.13.5)\n", "Requirement already satisfied: pandas in /usr/local/lib/python3.12/dist-packages (2.2.2)\n", "Requirement already satisfied: matplotlib in /usr/local/lib/python3.12/dist-packages (3.10.0)\n", "Requirement already satisfied: seaborn in /usr/local/lib/python3.12/dist-packages (0.13.2)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (2.0.2)\n", "Requirement already satisfied: textblob in /usr/local/lib/python3.12/dist-packages (0.19.0)\n", "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4) (2.8.3)\n", "Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.12/dist-packages (from beautifulsoup4) (4.15.0)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas) (2.9.0.post0)\n", "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.2)\n", "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas) (2025.3)\n", "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.3.3)\n", "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (0.12.1)\n", "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (4.62.0)\n", "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (1.5.0)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (26.0)\n", "Requirement already satisfied: pillow>=8 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (11.3.0)\n", "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.12/dist-packages (from matplotlib) (3.3.2)\n", "Requirement already satisfied: nltk>=3.9 in /usr/local/lib/python3.12/dist-packages (from textblob) (3.9.1)\n", "Requirement already satisfied: click in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (8.3.1)\n", "Requirement already satisfied: joblib in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (1.5.3)\n", "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (2025.11.3)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (from nltk>=3.9->textblob) (4.67.3)\n", "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n" ] } ], "source": [ "!pip install beautifulsoup4 pandas matplotlib seaborn numpy textblob" ] }, { "cell_type": "markdown", "metadata": { "id": "lquNYCbfL9IM" }, "source": [ "## **2.** ⛏ Web-scrape all book titles, prices, and ratings from books.toscrape.com" ] }, { "cell_type": "markdown", "metadata": { "id": "0IWuNpxxYDJF" }, "source": [ "### *a. Initial setup*\n", "Define the base url of the website you will scrape as well as how and what you will scrape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "91d52125" }, "outputs": [], "source": [ "import requests\n", "from bs4 import BeautifulSoup\n", "import pandas as pd\n", "import time\n", "\n", "base_url = \"https://books.toscrape.com/catalogue/page-{}.html\"\n", "headers = {\"User-Agent\": \"Mozilla/5.0\"}\n", "\n", "titles, prices, ratings = [], [], []" ] }, { "cell_type": "markdown", "metadata": { "id": "oCdTsin2Yfp3" }, "source": [ "### *b. Fill titles, prices, and ratings from the web pages*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xqO5Y3dnYhxt" }, "outputs": [], "source": [ "# Loop through all 50 pages\n", "for page in range(1, 51):\n", " url = base_url.format(page)\n", " response = requests.get(url, headers=headers)\n", " soup = BeautifulSoup(response.content, \"html.parser\")\n", " books = soup.find_all(\"article\", class_=\"product_pod\")\n", "\n", " for book in books:\n", " titles.append(book.h3.a[\"title\"])\n", " prices.append(float(book.find(\"p\", class_=\"price_color\").text[1:]))\n", " ratings.append(book.p.get(\"class\")[1])\n", "\n", " time.sleep(0.5) # polite scraping delay" ] }, { "cell_type": "code", "source": [ "len(titles), len(prices), len(ratings)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2RfLW0u7iksi", "outputId": "691adf75-63ef-4c4d-9f1a-1690c5d97a5c" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(1000, 1000, 1000)" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "metadata": { "id": "T0TOeRC4Yrnn" }, "source": [ "### *c. ✋🏻🛑⛔️ Create a dataframe df_books that contains the now complete \"title\", \"price\", and \"rating\" objects*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "l5FkkNhUYTHh", "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "outputId": "bf5b4f2d-330c-485d-ea69-ee8302d19be0" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " title price rating\n", "0 A Light in the Attic 51.77 Three\n", "1 Tipping the Velvet 53.74 One\n", "2 Soumission 50.10 One\n", "3 Sharp Objects 47.82 Four\n", "4 Sapiens: A Brief History of Humankind 54.23 Five" ], "text/html": [ "\n", "
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titlepricerating
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_books", "summary": "{\n \"name\": \"df_books\",\n \"rows\": 1000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 999,\n \"samples\": [\n \"The Grownup\",\n \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n \"Ayumi's Violin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.446689669952772,\n \"min\": 10.0,\n \"max\": 59.99,\n \"num_unique_values\": 903,\n \"samples\": [\n 19.73,\n 55.65,\n 46.31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"One\",\n \"Two\",\n \"Four\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 7 } ], "source": [ "df_books = pd.DataFrame({\n", " \"title\": titles,\n", " \"price\": prices,\n", " \"rating\": ratings\n", "})\n", "\n", "df_books.head()" ] }, { "cell_type": "code", "source": [ "df_books.to_csv(\"books.csv\", index=False)" ], "metadata": { "id": "sWumcBu3i6Mj" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!ls" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "CUPuxSv8jKoH", "outputId": "c3dea3ed-d6e1-4e1d-f3ef-c0871abb5ecf" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "books.csv sample_data\n" ] } ] }, { "cell_type": "code", "source": [ "df_books.to_excel(\"books.xlsx\", index=False)" ], "metadata": { "id": "R0qHyWBYjRb_" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!ls" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "97_5ABW5jdgH", "outputId": "0ef22899-9c10-4d95-d829-9380b7163c76" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "books.csv books.xlsx sample_data\n" ] } ] }, { "cell_type": "code", "source": [ "df_books.tail()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "id": "alqXE8F1jpg1", "outputId": "ea3e5ffa-e112-4c95-bfcf-97998f3389c6" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " title price rating\n", "995 Alice in Wonderland (Alice's Adventures in Won... 55.53 One\n", "996 Ajin: Demi-Human, Volume 1 (Ajin: Demi-Human #1) 57.06 Four\n", "997 A Spy's Devotion (The Regency Spies of London #1) 16.97 Five\n", "998 1st to Die (Women's Murder Club #1) 53.98 One\n", "999 1,000 Places to See Before You Die 26.08 Five" ], "text/html": [ "\n", "
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titlepricerating
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996Ajin: Demi-Human, Volume 1 (Ajin: Demi-Human #1)57.06Four
997A Spy's Devotion (The Regency Spies of London #1)16.97Five
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"df_books\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Ajin: Demi-Human, Volume 1 (Ajin: Demi-Human #1)\",\n \"1,000 Places to See Before You Die\",\n \"A Spy's Devotion (The Regency Spies of London #1)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 18.92949629546439,\n \"min\": 16.97,\n \"max\": 57.06,\n \"num_unique_values\": 5,\n \"samples\": [\n 57.06,\n 26.08,\n 16.97\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"One\",\n \"Four\",\n \"Five\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "markdown", "metadata": { "id": "duI5dv3CZYvF" }, "source": [ "### *d. Save web-scraped dataframe either as a CSV or Excel file*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lC1U_YHtZifh" }, "outputs": [], "source": [ "# 💾 Save to CSV\n", "df_books.to_csv(\"books_data.csv\", index=False)\n", "\n", "# 💾 Or save to Excel\n", "# df_books.to_excel(\"books_data.xlsx\", index=False)" ] }, { "cell_type": "markdown", "metadata": { "id": "qMjRKMBQZlJi" }, "source": [ "### *e. ✋🏻🛑⛔️ View first fiew lines*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "O_wIvTxYZqCK", "outputId": "349b36b0-c008-4fd5-d4a4-dba38ae18337" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " title price rating\n", "0 A Light in the Attic 51.77 Three\n", "1 Tipping the Velvet 53.74 One\n", "2 Soumission 50.10 One\n", "3 Sharp Objects 47.82 Four\n", "4 Sapiens: A Brief History of Humankind 54.23 Five" ], "text/html": [ "\n", "
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titlepricerating
0A Light in the Attic51.77Three
1Tipping the Velvet53.74One
2Soumission50.10One
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4Sapiens: A Brief History of Humankind54.23Five
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_books", "summary": "{\n \"name\": \"df_books\",\n \"rows\": 1000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 999,\n \"samples\": [\n \"The Grownup\",\n \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n \"Ayumi's Violin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.446689669952772,\n \"min\": 10.0,\n \"max\": 59.99,\n \"num_unique_values\": 903,\n \"samples\": [\n 19.73,\n 55.65,\n 46.31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"One\",\n \"Two\",\n \"Four\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 6 } ], "source": [] }, { "cell_type": "markdown", "metadata": { "id": "p-1Pr2szaqLk" }, "source": [ "## **3.** 🧩 Create a meaningful connection between real & synthetic datasets" ] }, { "cell_type": "markdown", "metadata": { "id": "SIaJUGIpaH4V" }, "source": [ "### *a. Initial setup*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-gPXGcRPuV_9" }, "outputs": [], "source": [ "import numpy as np\n", "import random\n", "from datetime import datetime\n", "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "random.seed(2025)\n", "np.random.seed(2025)" ] }, { "cell_type": "markdown", "metadata": { "id": "pY4yCoIuaQqp" }, "source": [ "### *b. Generate popularity scores based on rating (with some randomness) with a generate_popularity_score function*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mnd5hdAbaNjz" }, "outputs": [], "source": [ "def generate_popularity_score(rating):\n", " base = {\"One\": 2, \"Two\": 3, \"Three\": 3, \"Four\": 4, \"Five\": 4}.get(rating, 3)\n", " trend_factor = random.choices([-1, 0, 1], weights=[1, 3, 2])[0]\n", " return int(np.clip(base + trend_factor, 1, 5))" ] }, { "cell_type": "markdown", "metadata": { "id": "n4-TaNTFgPak" }, "source": [ "### *c. ✋🏻🛑⛔️ Run the function to create a \"popularity_score\" column from \"rating\"*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "V-G3OCUCgR07", "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "outputId": "0f163540-194b-41a3-e4bf-6544088484df" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " title price rating popularity_score\n", "0 A Light in the Attic 51.77 Three 3\n", "1 Tipping the Velvet 53.74 One 2\n", "2 Soumission 50.10 One 2\n", "3 Sharp Objects 47.82 Four 4\n", "4 Sapiens: A Brief History of Humankind 54.23 Five 3" ], "text/html": [ "\n", "
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titlepriceratingpopularity_score
0A Light in the Attic51.77Three3
1Tipping the Velvet53.74One2
2Soumission50.10One2
3Sharp Objects47.82Four4
4Sapiens: A Brief History of Humankind54.23Five3
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_books", "summary": "{\n \"name\": \"df_books\",\n \"rows\": 1000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 999,\n \"samples\": [\n \"The Grownup\",\n \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n \"Ayumi's Violin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.446689669952772,\n \"min\": 10.0,\n \"max\": 59.99,\n \"num_unique_values\": 903,\n \"samples\": [\n 19.73,\n 55.65,\n 46.31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"One\",\n \"Two\",\n \"Four\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"popularity_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 5,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 15 } ], "source": [ "df_books[\"popularity_score\"] = df_books[\"rating\"].apply(generate_popularity_score)\n", "\n", "df_books.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "HnngRNTgacYt" }, "source": [ "### *d. Decide on the sentiment_label based on the popularity score with a get_sentiment function*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kUtWmr8maZLZ" }, "outputs": [], "source": [ "def get_sentiment(popularity_score):\n", " if popularity_score <= 2:\n", " return \"negative\"\n", " elif popularity_score == 3:\n", " return \"neutral\"\n", " else:\n", " return \"positive\"" ] }, { "cell_type": "markdown", "metadata": { "id": "HF9F9HIzgT7Z" }, "source": [ "### *e. ✋🏻🛑⛔️ Run the function to create a \"sentiment_label\" column from \"popularity_score\"*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tafQj8_7gYCG", "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "outputId": "fbb79831-c1b8-4f2d-e502-79100a8314b4" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " title price rating popularity_score \\\n", "0 A Light in the Attic 51.77 Three 3 \n", "1 Tipping the Velvet 53.74 One 2 \n", "2 Soumission 50.10 One 2 \n", "3 Sharp Objects 47.82 Four 4 \n", "4 Sapiens: A Brief History of Humankind 54.23 Five 3 \n", "\n", " sentiment_label \n", "0 neutral \n", "1 negative \n", "2 negative \n", "3 positive \n", "4 neutral " ], "text/html": [ "\n", "
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titlepriceratingpopularity_scoresentiment_label
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_books", "summary": "{\n \"name\": \"df_books\",\n \"rows\": 1000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 999,\n \"samples\": [\n \"The Grownup\",\n \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n \"Ayumi's Violin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 14.446689669952772,\n \"min\": 10.0,\n \"max\": 59.99,\n \"num_unique_values\": 903,\n \"samples\": [\n 19.73,\n 55.65,\n 46.31\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rating\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"One\",\n \"Two\",\n \"Four\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"popularity_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 5,\n \"num_unique_values\": 5,\n \"samples\": [\n 2,\n 5,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment_label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 17 } ], "source": [ "df_books[\"sentiment_label\"] = df_books[\"popularity_score\"].apply(get_sentiment)\n", "\n", "df_books.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "T8AdKkmASq9a" }, "source": [ "## **4.** 📈 Generate synthetic book sales data of 18 months" ] }, { "cell_type": "markdown", "metadata": { "id": "OhXbdGD5fH0c" }, "source": [ "### *a. Create a generate_sales_profit function that would generate sales patterns based on sentiment_label (with some randomness)*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qkVhYPXGbgEn" }, "outputs": [], "source": [ "def generate_sales_profile(sentiment):\n", " months = pd.date_range(end=datetime.today(), periods=18, freq=\"M\")\n", "\n", " if sentiment == \"positive\":\n", " base = random.randint(200, 300)\n", " trend = np.linspace(base, base + random.randint(20, 60), len(months))\n", " elif sentiment == \"negative\":\n", " base = random.randint(20, 80)\n", " trend = np.linspace(base, base - random.randint(10, 30), len(months))\n", " else: # neutral\n", " base = random.randint(80, 160)\n", " trend = np.full(len(months), base + random.randint(-10, 10))\n", "\n", " seasonality = 10 * np.sin(np.linspace(0, 3 * np.pi, len(months)))\n", " noise = np.random.normal(0, 5, len(months))\n", " monthly_sales = np.clip(trend + seasonality + noise, a_min=0, a_max=None).astype(int)\n", "\n", " return list(zip(months.strftime(\"%Y-%m\"), monthly_sales))" ] }, { "cell_type": "markdown", "metadata": { "id": "L2ak1HlcgoTe" }, "source": [ "### *b. Run the function as part of building sales_data*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SlJ24AUafoDB" }, "outputs": [], "source": [ "sales_data = []\n", "for _, row in df_books.iterrows():\n", " records = generate_sales_profile(row[\"sentiment_label\"])\n", " for month, units in records:\n", " sales_data.append({\n", " \"title\": row[\"title\"],\n", " \"month\": month,\n", " \"units_sold\": units,\n", " \"sentiment_label\": row[\"sentiment_label\"]\n", " })" ] }, { "cell_type": "markdown", "metadata": { "id": "4IXZKcCSgxnq" }, "source": [ "### *c. ✋🏻🛑⛔️ Create a df_sales DataFrame from sales_data*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wcN6gtiZg-ws", "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "outputId": "0923c679-8e5d-4b12-889f-181996aa4973" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " title month units_sold sentiment_label\n", "0 A Light in the Attic 2024-09 100 neutral\n", "1 A Light in the Attic 2024-10 109 neutral\n", "2 A Light in the Attic 2024-11 102 neutral\n", "3 A Light in the Attic 2024-12 107 neutral\n", "4 A Light in the Attic 2025-01 108 neutral" ], "text/html": [ "\n", "
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titlemonthunits_soldsentiment_label
0A Light in the Attic2024-09100neutral
1A Light in the Attic2024-10109neutral
2A Light in the Attic2024-11102neutral
3A Light in the Attic2024-12107neutral
4A Light in the Attic2025-01108neutral
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df_sales", "summary": "{\n \"name\": \"df_sales\",\n \"rows\": 18000,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 999,\n \"samples\": [\n \"The Grownup\",\n \"Persepolis: The Story of a Childhood (Persepolis #1-2)\",\n \"Ayumi's Violin\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"month\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 18,\n \"samples\": [\n \"2024-09\",\n \"2024-10\",\n \"2025-05\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"units_sold\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 98,\n \"min\": 0,\n \"max\": 362,\n \"num_unique_values\": 354,\n \"samples\": [\n 214,\n 289,\n 205\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment_label\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"neutral\",\n \"negative\",\n \"positive\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 20 } ], "source": [ "df_sales = pd.DataFrame(sales_data)\n", "\n", "df_sales.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "EhIjz9WohAmZ" }, "source": [ "### *d. Save df_sales as synthetic_sales_data.csv & view first few lines*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MzbZvLcAhGaH", "outputId": "a99b62dd-10e6-4ade-d482-61807d61559c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " title month units_sold sentiment_label\n", "0 A Light in the Attic 2024-09 100 neutral\n", "1 A Light in the Attic 2024-10 109 neutral\n", "2 A Light in the Attic 2024-11 102 neutral\n", "3 A Light in the Attic 2024-12 107 neutral\n", "4 A Light in the Attic 2025-01 108 neutral\n" ] } ], "source": [ "df_sales.to_csv(\"synthetic_sales_data.csv\", index=False)\n", "\n", "print(df_sales.head())" ] }, { "cell_type": "markdown", "metadata": { "id": "7g9gqBgQMtJn" }, "source": [ "## **5.** 🎯 Generate synthetic customer reviews" ] }, { "cell_type": "markdown", "metadata": { "id": "Gi4y9M9KuDWx" }, "source": [ "### *a. ✋🏻🛑⛔️ Ask ChatGPT to create a list of 50 distinct generic book review texts for the sentiment labels \"positive\", \"neutral\", and \"negative\" called synthetic_reviews_by_sentiment*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "b3cd2a50" }, "outputs": [], "source": [ "synthetic_reviews_by_sentiment = {\n", "\n", " \"positive\": [\n", " \"An unforgettable story that kept me hooked until the last page.\",\n", " \"Beautifully written and deeply moving.\",\n", " \"A masterpiece with rich characters and a gripping plot.\",\n", " \"Absolutely loved every chapter of this book.\",\n", " \"The storytelling was immersive and emotionally powerful.\",\n", " \"A brilliant read that exceeded my expectations.\",\n", " \"Engaging from start to finish.\",\n", " \"The author crafted a truly inspiring narrative.\",\n", " \"One of the most satisfying reads this year.\",\n", " \"A compelling journey filled with heart and depth.\",\n", " \"The characters felt real and relatable.\",\n", " \"An uplifting and rewarding experience.\",\n", " \"The pacing and development were spot on.\",\n", " \"A thoughtful and beautifully executed story.\",\n", " \"Highly entertaining and well-structured.\",\n", " \"An emotionally resonant and memorable read.\",\n", " \"A book I would happily recommend to anyone.\",\n", " \"The plot twists were clever and exciting.\",\n", " \"An outstanding example of great storytelling.\",\n", " \"Rich themes explored with sensitivity and care.\",\n", " \"The dialogue felt natural and impactful.\",\n", " \"A captivating story that stayed with me.\",\n", " \"Strong writing and a satisfying conclusion.\",\n", " \"An impressive and engaging narrative.\",\n", " \"The emotional depth was remarkable.\",\n", " \"A well-crafted and immersive experience.\",\n", " \"The story flowed seamlessly.\",\n", " \"An inspiring and meaningful book.\",\n", " \"Exceptionally well-written.\",\n", " \"A delightful and heartwarming read.\",\n", " \"The characters were unforgettable.\",\n", " \"An exciting and beautifully told story.\",\n", " \"A powerful and moving journey.\",\n", " \"Highly engaging and emotionally rich.\",\n", " \"The world-building was fantastic.\",\n", " \"A rewarding and thoughtful book.\",\n", " \"Creative, compelling, and inspiring.\",\n", " \"A truly enjoyable experience.\",\n", " \"One of my favorite reads lately.\",\n", " \"A strong and satisfying plot.\",\n", " \"Expertly written and deeply engaging.\",\n", " \"An absorbing and powerful story.\",\n", " \"The narrative was captivating throughout.\",\n", " \"A standout book in its genre.\",\n", " \"Brilliant pacing and development.\",\n", " \"An emotionally fulfilling read.\",\n", " \"A deeply engaging storyline.\",\n", " \"A fresh and exciting perspective.\",\n", " \"Compelling and beautifully executed.\",\n", " \"An excellent and inspiring book.\"\n", " ],\n", "\n", " \"neutral\": [\n", " \"An average read overall.\",\n", " \"It was fine but nothing extraordinary.\",\n", " \"Some parts were interesting, others less so.\",\n", " \"A decent way to spend the time.\",\n", " \"Neither great nor terrible.\",\n", " \"The story had some strong moments.\",\n", " \"It met my expectations but did not exceed them.\",\n", " \"An okay book with mixed elements.\",\n", " \"Some chapters stood out more than others.\",\n", " \"A fairly standard storyline.\",\n", " \"It was readable but not remarkable.\",\n", " \"A moderate and balanced experience.\",\n", " \"The pacing felt uneven at times.\",\n", " \"A reasonable read for the genre.\",\n", " \"The characters were somewhat engaging.\",\n", " \"Not bad, but not particularly memorable.\",\n", " \"A straightforward narrative.\",\n", " \"It had potential but felt average.\",\n", " \"The ending was acceptable.\",\n", " \"An ordinary reading experience.\",\n", " \"It held my attention in parts.\",\n", " \"The writing was serviceable.\",\n", " \"A mildly enjoyable book.\",\n", " \"The themes were explored adequately.\",\n", " \"Some predictable plot elements.\",\n", " \"An acceptable but simple story.\",\n", " \"It was fine for casual reading.\",\n", " \"Nothing especially surprising.\",\n", " \"A middle-of-the-road experience.\",\n", " \"The plot was easy to follow.\",\n", " \"A safe and familiar story.\",\n", " \"It had both strengths and weaknesses.\",\n", " \"An average addition to the genre.\",\n", " \"Readable but not outstanding.\",\n", " \"Somewhat engaging throughout.\",\n", " \"It did not leave a strong impression.\",\n", " \"A balanced but unremarkable read.\",\n", " \"The writing was consistent.\",\n", " \"A competent but simple narrative.\",\n", " \"An okay book overall.\",\n", " \"Moderately entertaining.\",\n", " \"It had a few standout moments.\",\n", " \"Neither disappointing nor impressive.\",\n", " \"A fairly typical storyline.\",\n", " \"A predictable but decent read.\",\n", " \"The characters were adequate.\",\n", " \"It was fine for what it was.\",\n", " \"Some good ideas, average execution.\",\n", " \"An acceptable reading experience.\",\n", " \"Overall, just okay.\"\n", " ],\n", "\n", " \"negative\": [\n", " \"I struggled to stay engaged.\",\n", " \"The plot felt confusing and disjointed.\",\n", " \"The characters lacked depth.\",\n", " \"It did not meet my expectations.\",\n", " \"The pacing was slow and uneven.\",\n", " \"I found it difficult to finish.\",\n", " \"The story felt underdeveloped.\",\n", " \"Disappointing overall.\",\n", " \"The writing style did not work for me.\",\n", " \"It lacked emotional impact.\",\n", " \"The plot twists felt forced.\",\n", " \"I expected much more from this book.\",\n", " \"The narrative felt flat.\",\n", " \"The characters were forgettable.\",\n", " \"It was hard to stay interested.\",\n", " \"The ending was unsatisfying.\",\n", " \"The dialogue felt unnatural.\",\n", " \"The story lacked direction.\",\n", " \"Not an enjoyable experience.\",\n", " \"It failed to capture my attention.\",\n", " \"The themes were poorly explored.\",\n", " \"The execution felt weak.\",\n", " \"A frustrating read.\",\n", " \"The structure felt messy.\",\n", " \"I would not recommend this book.\",\n", " \"It felt rushed and incomplete.\",\n", " \"The story lacked coherence.\",\n", " \"The pacing dragged too much.\",\n", " \"The character development was weak.\",\n", " \"It was difficult to connect with the story.\",\n", " \"The narrative felt repetitive.\",\n", " \"An underwhelming experience.\",\n", " \"The writing lacked polish.\",\n", " \"It did not deliver on its promise.\",\n", " \"The plot felt predictable and dull.\",\n", " \"I lost interest halfway through.\",\n", " \"The book felt overly long.\",\n", " \"The storyline was disappointing.\",\n", " \"The tension never built properly.\",\n", " \"It felt uninspired.\",\n", " \"The emotional depth was missing.\",\n", " \"A forgettable and weak read.\",\n", " \"The story lacked originality.\",\n", " \"It was not worth the time.\",\n", " \"The characters felt shallow.\",\n", " \"The writing was inconsistent.\",\n", " \"The conclusion was abrupt.\",\n", " \"The plot was hard to follow.\",\n", " \"Overall, a disappointing book.\",\n", " \"It simply did not work for me.\"\n", " ]\n", "}" ] }, { "cell_type": "markdown", "metadata": { "id": "fQhfVaDmuULT" }, "source": [ "### *b. Generate 10 reviews per book using random sampling from the corresponding 50*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "l2SRc3PjuTGM" }, "outputs": [], "source": [ "review_rows = []\n", "for _, row in df_books.iterrows():\n", " title = row['title']\n", " sentiment_label = row['sentiment_label']\n", " review_pool = synthetic_reviews_by_sentiment[sentiment_label]\n", " sampled_reviews = random.sample(review_pool, 10)\n", " for review_text in sampled_reviews:\n", " review_rows.append({\n", " \"title\": title,\n", " \"sentiment_label\": sentiment_label,\n", " \"review_text\": review_text,\n", " \"rating\": row['rating'],\n", " \"popularity_score\": row['popularity_score']\n", " })" ] }, { "cell_type": "markdown", "metadata": { "id": "bmJMXF-Bukdm" }, "source": [ "### *c. Create the final dataframe df_reviews & save it as synthetic_book_reviews.csv*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZUKUqZsuumsp" }, "outputs": [], "source": [ "df_reviews = pd.DataFrame(review_rows)\n", "df_reviews.to_csv(\"synthetic_book_reviews.csv\", index=False)" ] }, { "cell_type": "markdown", "source": [ "### *c. inputs for R*" ], "metadata": { "id": "_602pYUS3gY5" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3946e521", "outputId": "224addb0-6f3c-4e83-b7b6-ae9213486099" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "✅ Wrote synthetic_title_level_features.csv\n", "✅ Wrote synthetic_monthly_revenue_series.csv\n" ] } ], "source": [ "import numpy as np\n", "\n", "def _safe_num(s):\n", " return pd.to_numeric(\n", " pd.Series(s).astype(str).str.replace(r\"[^0-9.]\", \"\", regex=True),\n", " errors=\"coerce\"\n", " )\n", "\n", "# --- Clean book metadata (price/rating) ---\n", "df_books_r = df_books.copy()\n", "if \"price\" in df_books_r.columns:\n", " df_books_r[\"price\"] = _safe_num(df_books_r[\"price\"])\n", "if \"rating\" in df_books_r.columns:\n", " df_books_r[\"rating\"] = _safe_num(df_books_r[\"rating\"])\n", "\n", "df_books_r[\"title\"] = df_books_r[\"title\"].astype(str).str.strip()\n", "\n", "# --- Clean sales ---\n", "df_sales_r = df_sales.copy()\n", "df_sales_r[\"title\"] = df_sales_r[\"title\"].astype(str).str.strip()\n", "df_sales_r[\"month\"] = pd.to_datetime(df_sales_r[\"month\"], errors=\"coerce\")\n", "df_sales_r[\"units_sold\"] = _safe_num(df_sales_r[\"units_sold\"])\n", "\n", "# --- Clean reviews ---\n", "df_reviews_r = df_reviews.copy()\n", "df_reviews_r[\"title\"] = df_reviews_r[\"title\"].astype(str).str.strip()\n", "df_reviews_r[\"sentiment_label\"] = df_reviews_r[\"sentiment_label\"].astype(str).str.lower().str.strip()\n", "if \"rating\" in df_reviews_r.columns:\n", " df_reviews_r[\"rating\"] = _safe_num(df_reviews_r[\"rating\"])\n", "if \"popularity_score\" in df_reviews_r.columns:\n", " df_reviews_r[\"popularity_score\"] = _safe_num(df_reviews_r[\"popularity_score\"])\n", "\n", "# --- Sentiment shares per title (from reviews) ---\n", "sent_counts = (\n", " df_reviews_r.groupby([\"title\", \"sentiment_label\"])\n", " .size()\n", " .unstack(fill_value=0)\n", ")\n", "for lab in [\"positive\", \"neutral\", \"negative\"]:\n", " if lab not in sent_counts.columns:\n", " sent_counts[lab] = 0\n", "\n", "sent_counts[\"total_reviews\"] = sent_counts[[\"positive\", \"neutral\", \"negative\"]].sum(axis=1)\n", "den = sent_counts[\"total_reviews\"].replace(0, np.nan)\n", "sent_counts[\"share_positive\"] = sent_counts[\"positive\"] / den\n", "sent_counts[\"share_neutral\"] = sent_counts[\"neutral\"] / den\n", "sent_counts[\"share_negative\"] = sent_counts[\"negative\"] / den\n", "sent_counts = sent_counts.reset_index()\n", "\n", "# --- Sales aggregation per title ---\n", "sales_by_title = (\n", " df_sales_r.dropna(subset=[\"title\"])\n", " .groupby(\"title\", as_index=False)\n", " .agg(\n", " months_observed=(\"month\", \"nunique\"),\n", " avg_units_sold=(\"units_sold\", \"mean\"),\n", " total_units_sold=(\"units_sold\", \"sum\"),\n", " )\n", ")\n", "\n", "# --- Title-level features (join sales + books + sentiment) ---\n", "df_title = (\n", " sales_by_title\n", " .merge(df_books_r[[\"title\", \"price\", \"rating\"]], on=\"title\", how=\"left\")\n", " .merge(sent_counts[[\"title\", \"share_positive\", \"share_neutral\", \"share_negative\", \"total_reviews\"]],\n", " on=\"title\", how=\"left\")\n", ")\n", "\n", "df_title[\"avg_revenue\"] = df_title[\"avg_units_sold\"] * df_title[\"price\"]\n", "df_title[\"total_revenue\"] = df_title[\"total_units_sold\"] * df_title[\"price\"]\n", "\n", "df_title.to_csv(\"synthetic_title_level_features.csv\", index=False)\n", "print(\"✅ Wrote synthetic_title_level_features.csv\")\n", "\n", "# --- Monthly revenue series (proxy: units_sold * price) ---\n", "monthly_rev = (\n", " df_sales_r.merge(df_books_r[[\"title\", \"price\"]], on=\"title\", how=\"left\")\n", ")\n", "monthly_rev[\"revenue\"] = monthly_rev[\"units_sold\"] * monthly_rev[\"price\"]\n", "\n", "df_monthly = (\n", " monthly_rev.dropna(subset=[\"month\"])\n", " .groupby(\"month\", as_index=False)[\"revenue\"]\n", " .sum()\n", " .rename(columns={\"revenue\": \"total_revenue\"})\n", " .sort_values(\"month\")\n", ")\n", "# if revenue is all NA (e.g., missing price), fallback to units_sold as a teaching proxy\n", "if df_monthly[\"total_revenue\"].notna().sum() == 0:\n", " df_monthly = (\n", " df_sales_r.dropna(subset=[\"month\"])\n", " .groupby(\"month\", as_index=False)[\"units_sold\"]\n", " .sum()\n", " .rename(columns={\"units_sold\": \"total_revenue\"})\n", " .sort_values(\"month\")\n", " )\n", "\n", "df_monthly[\"month\"] = pd.to_datetime(df_monthly[\"month\"], errors=\"coerce\").dt.strftime(\"%Y-%m-%d\")\n", "df_monthly.to_csv(\"synthetic_monthly_revenue_series.csv\", index=False)\n", "print(\"✅ Wrote synthetic_monthly_revenue_series.csv\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "RYvGyVfXuo54" }, "source": [ "### *d. ✋🏻🛑⛔️ View the first few lines*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xfE8NMqOurKo", "outputId": "191730ba-d5e2-4df7-97d2-99feb0b704af" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " title sentiment_label \\\n", "0 A Light in the Attic neutral \n", "1 A Light in the Attic neutral \n", "2 A Light in the Attic neutral \n", "3 A Light in the Attic neutral \n", "4 A Light in the Attic neutral \n", "\n", " review_text rating popularity_score \n", "0 Had potential that went unrealized. Three 3 \n", "1 The themes were solid, but not well explored. Three 3 \n", "2 It simply lacked that emotional punch. Three 3 \n", "3 Serviceable but not something I'd go out of my... Three 3 \n", "4 Standard fare with some promise. Three 3 \n" ] } ], "source": [] }, { "cell_type": "code", "source": [ "len(df_reviews)" ], "metadata": { "id": "BYVLGLBlm5jn", "outputId": "c4f444a5-3638-456a-c357-53a9a7fd2c60", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "10000" ] }, "metadata": {}, "execution_count": 26 } ] } ], "metadata": { "colab": { "collapsed_sections": [ "jpASMyIQMaAq", "lquNYCbfL9IM", "0IWuNpxxYDJF", "oCdTsin2Yfp3", "T0TOeRC4Yrnn", "duI5dv3CZYvF", "qMjRKMBQZlJi", "p-1Pr2szaqLk", "SIaJUGIpaH4V", "pY4yCoIuaQqp", "n4-TaNTFgPak", "HnngRNTgacYt", "HF9F9HIzgT7Z", "T8AdKkmASq9a", "OhXbdGD5fH0c", "L2ak1HlcgoTe", "4IXZKcCSgxnq", "EhIjz9WohAmZ", "Gi4y9M9KuDWx", "fQhfVaDmuULT", "bmJMXF-Bukdm", "RYvGyVfXuo54" ], "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }